prediction models for diagnosis and prognosis of severe covid-19

Conditions: Coronavirus Infection; COVID19

Summary: clinical observation has found that covid-19 patients often present inconsistency of clinical features, nucleic acid of the sars-cov-2 and imaging findings, which brings challenges to the management of patients.the quantitative assessment of patients' pulmonary lesions of chest ct, combined with the basic information, epidemiological history, clinical symptoms, basic diseases and other information of patients, will quickly establish a reliable prediction model for the severe covid-19. this model will greatly contribute to the effective diagnosis and treatment of covid-19.

1. research purpose the research team collected the clinical and chest ct of 1,000 covid-19 patients from multiple hospitals. we plan to use these data to explore the imaging features of the covid-19 and develop a convenient, easy-to-use, highly reliable imaging ai model for detecting and predicting the severe covid-19. the model is used for imaging evaluation of covid-19 patients, in order to achieve the purpose of early diagnosis, reasonable management of patients and prediction of severe covid-19. 2. research design and methods: this research is a retrospective study. the project research period have 6 months. start time: the date of ethics approval. end time: august 20, 2020. 2.1 establish an ai model for the detection of covid-19 chest ct lesions based on existing models and data, rapid detection of lesions on the chest high-resolution ct (hrct) images of covid-19, identification of the character of the lesions including the volume of the lesions. 2.1.1 research data covid-19 group: 1,000 cases of covid-19 patients who were tested positive for nucleic acid of the sars-cov-2 in more than ten designated hospitals within and outside zhejiang province. all underwent chest hrct examination and relatively complete clinical and laboratory data. control group: patients with other viral and bacterial pneumonia. a collection of 1000 patients with other types of pneumonia in multiple centers, all underwent chest hrct examination and relatively complete clinical data. 2.1.2 research methods 1. lesion detection, segmentation and quantification. based on the artificial intelligence analysis function developed by yitu technology, the patient's chest ct image data is analyzed, including: a. detecting lung lesions; b. quantitative and radiomics analysis of key imaging features such as the shape, extent, and density of the lesions, and accurately calculating the cumulative pneumonia burden of the disease; c. for focal lesions, diffuse lesions, quantitative analysis of the severity of various pneumonia diseases involving the entire lung. 2. based on the above-mentioned lesion segmentation detection results, analyze the characteristic manifestations of the covid-19. observe the differences in the number, shape, range, density, and radiomics characteristics of lung lesions in patients with covid-19 and other pneumonia, and quantify their unique lung characteristics. compare and analyze the correlation of lung characteristics with clinical symptoms and guideline classification characteristics, and clarify the value of quantitative mathematical characteristics in auxiliary diagnosis classification. 2.2 establish an ai model for predicting severe covid-19 establish a reliable ai model for predicting severe covid-19 through chest ct imaging data of the patient, basic patient information, epidemiological history, clinical symptoms, and underlying diseases. it is planned to establish a severe covid-19 risk assessment system that can not only assist doctors in the critical evaluation of patients in hospital, but also warn the severe risk of patients in home isolation. the system will include simple and accurate models. the former only uses patient images, basic demographic characteristics, symptoms and other easy-to-collect information. this model may be used in wuhan city that has basic data for mild cases but has no treatment conditions. allow them to be isolated at home or other patients without medical resources, and early warning of the risk of severe transformation in the hospital system for preliminary testing, so as to facilitate subsequent patient management and treatment. the complex model will incorporate more complex and detailed information, such as multiple images of patients and blood test data, to establish a more accurate predictive model, which can be used to provide reference for the diagnosis and treatment strategies of patients in the hospital, and can prompt more covid-19 factors related to pneumonia. 2.2.1 research object 1,000 patients with mild cases of novel coronavirus pneumonia were diagnosed, and were divided into severe group and non-severe group based on subsequent clinical outcomes. definition of mild illness: the patient only showed symptoms such as fever and respiratory tract in general, and no severe symptoms occurred during the visit and follow-up. definition of severe illness: patients who have one of the following conditions during treatment: 1. respiratory distress, rr≥30 beats/min; 2. in resting state, mean oxygen saturation≤93%; 3. arterial oxygen partial pressure ( pao2)/inhalation oxygen concentration (fio2) ≤300mmhg (1mmhg=0.133kpa); 4. respiratory failure occurs and mechanical ventilation is required; 5. shock occurs; 6. icu monitoring and treatment is required for combined other organ failure. 2.2.2 research methods this study uses artificial intelligence technology to predict the severity of patients with mild illness. the data required for its modeling comes from multiple sources: (1) using radiomics analysis technology, extract the ct image of the patient including the chest ct value, shape and size of the lesion high-dimensional imaging radiomics features such as texture and wavelet features to obtain more accurate and comprehensive image data information. yitu's existing imaging raidomics feature extraction tool can extract up to 5,900 ct image features to make lung state more accurate assessment and prediction it becomes possible; (2) collecting information on the subjective evaluation of ct image signs by imaging doctors, as well as multi-dimensional information such as basic patient information, disease history, laboratory test results, and clinical symptoms; (3) based on the developed chest ct image analysis function to extract quantitative parameters such as pneumonia load index, patch semi-quantitative information and so on using deep learning technology. the total collected data set is divided into training set and internal verification set. firstly, the information is analyzed by traditional medicine and multi-dimensional ai algorithm, comprehensively and quantitatively analyze whether the data column is included in the prediction model and the weight in the model, and look for strongly related factors. try to use machine learning, deep learning and other ai algorithms to establish a risk prediction model for severe covid-19. and the results output the probability of the patient's severity, and classify the risk of the patient's severity. evaluate the model's ability to identify high-risk and low-risk patients with indicators such as sensitivity, specificity, and preliminarily verify the stability of the model. 2.2.3 clinical application verification after the prediction model is established, the prediction model will continue to be used in the subsequent multi-center collection of supplementary clinical patient data, use patient follow-up data to verify its sensitivity and specificity, and continuously incorporate the newly collected data into the model training set to continuously improve the prediction model. improve the application efficiency of risk assessment models. in the end, it will reduce the conversion rate of severe patients, reduce the management pressure of mild patients, and better assist doctors in clinical diagnosis and treatment decision-making and patient management.

Eligibility criteria:
inclusion criteria: * the patient was tested positive for nucleic acid of the sars-cov-2 exclusion criteria: * none